Master the key concepts of Hadoop and Big data. Join and learn from a Big Data/Hadoop expert.
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Corporate Served- Big Data Hadoop is one of the most popular open-source utility software platforms that help store a huge amount of data and also has a massive processing power in it. It can multitask, i.e., it can handle unlimited data simultaneously.
- Things you will learn:
- If you want to enhance your career potential, then Big Data Hadoop could be an ideal choice. so, what are you waiting for Simply enrol for the best Big Data Hadoop course without breaking your bank.
In-depth knowledge of Big Data Hadoop features like HDFS, YARN, Hive, Map Reduce, Pig, Spark.
Get the opportunity to work on real-life industry cases.
Master the desired skill with or without any technical background.
Big Data Hadoop is the most in-demand skills at present.
Popular Big Data job titles Developer, Administrator, Data Analyst, Tester, and Solution Architect.
Big Data Hadoop Training
- With our Big Data Hadoop training and certification program online, you are likely to learn a multitude of skills and implement them at work. The main objective of our training program is:
Learn YARN, Map Reduce, and become an expert in writing apps using the mentioned tools.
To impart an in-depth knowledge of HDFS, Sqoop, Pig, Hive, Oozie, shell scripting, Spark, Flume, Zookeeper online.
You will understand Big Data Hadoop cluster and learn Big Data Hadoop analytics as well.
Configure various ETL tools and know-how to set the pseudo nodes.
Work on real-life projects and hands-on assignments to facilitate the learning process.
- According to the recent survey conducted, the estimated salary of a Big Data Hadoop Developer ranges between $119,250 to $168,250 per year. when you choose us for your Big Data Hadoop training, you are likely to get higher salary packages, unlike other IT departments.
- So, acknowledge the coming opportunity, focus on sharpening the required skills and get ready to grasp the chance when it comes.
Big Data Analytics is proven to be the most preferred software or data analytic tool.
These tools are considered to increase the efficiency of any organization.
Organizations use Big Data Analytic tools to get better insights into their sales.
Big data is also helping businesses to promote marketing on social media to meet their goals.
There is an acute shortage of skilled analysts and managers in the coming years.
- Our Big Data Hadoop training is backed by accredited experts along with the facility of real-time sessions. After the completion of your course, you will get significant career growth;
Our Big Data Hadoop training lessons include the perfect combination of theoretical as well as practical components to make the training conducive for learning.
After completing the basic level Big Data Hadoop training, learners are qualified for the next level and thus are prepared for it.
In Big Data Hadoop training, individuals are provided with the basic concepts to recap and sharpen their skills.
Get Big Data Hadoop Live sessions by providing recordings of the live class, study material, ppt, projects, etc.
Having proper training and the right certification can reap you a perfect result.
- Big Data Hadoop is one of the first frameworks for extracting and transforming huge volumes of data and hence is widely used making it the most popular big data framework. The best part of the Big Data Hadoop system is its flexibility to control the overall system and store data in HDFS and supports data compression. Data can be installed using tools such as Map Reduce, Pig, and Hive.
There is a great demand for Big Data Managers and it will increase with time.
It is predicted by 2022, the Big Data Hadoop market will reach $99.31.
In the US, there is a shortage of Big Data Hadoop skilled experts up to 1.6 million.
Almost every top MNCs are adopting the Big Data Hadoop system.
Get the most outstanding learning experience from Coma Campus.
- Here are some job roles and responsibilities covered as a part of the Big Data Hadoop training program:
You should know how to develop, implement and maintain Big Data solutions.
You should conduct database modelling and development, data mining, and warehousing.
You must be well-versed in designing and deploying data platforms.
You should know to improve data efficiency, reliability & quality, create data enrichment, build high performance, etc.
You must have substantial knowledge in MongoDB, RDBMS, Spark core, HBase or Cassandra, Pig, Yarn, SQL, etc.
You must take a pivotal part in the development and deployment of innovative big data platforms.
- One of the remarkable flairs of Big Data Hadoop is the ease with which it can be scaled up. The capability of processing a large amount of data in very little time makes it even more appealing.
- Many top MNCs are on the lookout for professional Big Data and Analytics for their businesses.
You can clear any certification exam successfully.
You will be offered a certificate that is accepted worldwide.
Take up a lucrative and promising career ahead.
Increase your worth in today’s tech-driven landscape.
Our certification will let you bag higher salary packages.
- Training is vital to future proof your employability but getting the right training will cement your position. Our Big Data Hadoop certification training may help you learn Data Hadoop concepts without any difficulty.
Help you clear any certification exam successfully.
After the course, you will be offered a certificate thereafter.
You can establish yourself as a certified Developer, Administrator, Data Analyst, Tester, or Solution Architect.
Register now and ramp up your career and own it.
Why Should You Learn Big Data Hadoop?
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Big Data Hadoop Training
- Croma Campus offers the best Hadoop development Training in Noida with most experienced professionals. Our Instructors are working in Big Data Space and related technologies for years in MNC's.
- We aware of industry needs and we are offering Hadoop development Training in more practical way. Our team of Hadoop trainers offers the in-classroom training with best industry practices.
- We framed our syllabus to match with the real-world requirements for beginner level to advanced level. Our training will be handled in either weekday or weekends programme depends on participants requirements.
- In this program you will learn:
Introduction to Big Data & Hadoop
HDFS
YARN
Managing and Scheduling Jobs
Apache Sqoop
Apache Flume
Getting Data into HDFS
Apache Kafka
Hadoop Clients
Cluster Maintenance
Cloudera Manager
Cluster Monitoring and Troubleshooting
Planning Your Hadoop Cluster
Advanced Cluster Configuration
MapReduce Framework
Apache PIG
Apache HIVE
No SQL Databases HBase
Functional Programming using Scala
Apache Spark
Hadoop Datawarehouse
Writing MapReduce Program
Introduction to Combiner
Problem-solving with MapReduce
- Introduction to Big Data
Overview of Course
What is Big Data
Big Data Analytics
Challenges of Traditional System
Distributed Systems
- Introduction to Hadoop
Components of Hadoop Ecosystem
Commercial Hadoop Distributions
Why Hadoop
Fundamental Concepts in Hadoop
- Security in Hadoop
Why Hadoop Security Is Important
Hadoop’s Security System Concepts
What Kerberos Is and How it Works
Securing a Hadoop Cluster with Kerberos
- Initial Setup and Configuration
Deployment Types
Installing Hadoop
Specifying the Hadoop Configuration
Performing Initial HDFS Configuration
Performing Initial YARN and MapReduce Configuration
Hadoop Logging
- HDFS
What is HDFS
Need for HDFS
Regular File System vs HDFS
Characteristics of HDFS
HDFS Architecture and Components
High Availability Cluster Implementations
HDFS Component File System Namespace
Data Block Split
Data Replication Topology
HDFS Command Line
- YARN
Yarn Introduction
Yarn Use Case
Yarn and its Architecture
Resource Manager
How Resource Manager Operates
Application Master
How Yarn Runs an Application
Tools for Yarn Developers
- Managing and Scheduling Jobs
Managing Running Jobs
Scheduling Hadoop Jobs
Configuring the Fair Scheduler
Impala Query Scheduling
- Apache Sqoop
Apache Sqoop
Sqoop and Its Uses
Sqoop Processing
Sqoop Import Process
Sqoop Connectors
Importing and Exporting Data from MySQL to HDFS
- Apache Sqoop
Apache Flume
Flume Model
Scalability in Flume
Components in Flume’s Architecture
Configuring Flume Components
Ingest Twitter Data
- Getting Data into HDFS
Data Ingestion Overview
Ingesting Data from External Sources with Flume
Ingesting Data from Relational Databases with Sqoop
REST Interfaces
Best Practices for Importing Data
- Apache Kafka
Apache Kafka
Aggregating User Activity Using Kafka
Kafka Data Model
Partitions
Apache Kafka Architecture
Setup Kafka Cluster
Producer Side API Example
Consumer Side API
Consumer Side API Example
Kafka Connect
- Hadoop Clients
What is a Hadoop Client
Installing and Configuring Hadoop Clients
Installing and Configuring Hue
Hue Authentication and Authorization
- Cluster Maintenance
Checking HDFS Status
Copying Data between Clusters
Adding and Removing Cluster Nodes
Rebalancing the Cluster
Cluster Upgrading
- Cloudera Manager
The Motivation for Cloudera Manager
Cloudera Manager Features
Express and Enterprise Versions
Cloudera Manager Topology
Installing Cloudera Manager
Installing Hadoop Using Cloudera Manager
Performing Basic Administration Tasks using Cloudera Manager
- Cluster Monitoring and Troubleshooting
General System Monitoring
Monitoring Hadoop Clusters
Common Troubleshooting Hadoop Clusters
Common Misconfigurations
- Planning Your Hadoop Cluster
General Planning Considerations
Choosing the Right Hardware
Network Considerations
Configuring Nodes
Planning for Cluster Management
- Advanced Cluster Configuration
Advanced Configuration Parameters
Configuring Hadoop Ports
Explicitly Including and Excluding Hosts
Configuring HDFS for Rack Awareness
Configuring HDFS High Availability
- MapReduce Framework
What is MapReduce
Basic MapReduce Concepts
Distributed Processing in MapReduce
Word Count Example
Map Execution Phases
Map Execution Distributed Two Node Environment
MapReduce Jobs
Hadoop MapReduce Job Work Interaction
Setting Up the Environment for MapReduce Development
Set of Classes
Creating a New Project
Advanced MapReduce
Data Types in Hadoop
Output formats in MapReduce
Using Distributed Cache
Joins in MapReduce
Replicated Join
- Apache PIG
Introduction to Pig
Components of Pig
Pig Data Model
Pig Interactive Modes
Pig Operations
Various Relations Performed by Developers
- Apache HIVE
Introduction to Apache Hive
Hive SQL over Hadoop MapReduce
Hive Architecture
Interfaces to Run Hive Queries
Running Beeline from Command Line
Hive Meta Store
Hive DDL and DML
Creating New Table
Data Types
Validation of Data
File Format Types
Data Serialization
Hive Table and Avro Schema
Hive Optimization Partitioning Bucketing and Sampling
Non-Partitioned Table
Data Insertion
Dynamic Partitioning in Hive
Bucketing
What Do Buckets Do
Hive Analytics UDF and UDAF
Other Functions of Hive
- No SQL Databases HBase
NoSQL Databases HBase
NoSQL Introduction
HBase Overview
HBase Architecture
Data Model
Connecting to HBase
HBase Shell
- Functional Programming using Scala
Basics of Functional Programming and Scala
Introduction to Scala
Scala Installation
Functional Programming
Programming with Scala
Basic Literals and Arithmetic Programming
Logical Operators
Type Inference Classes Objects and Functions in Scala
Type Inference Functions Anonymous Function and Class
Collections
Types of Collections
Operations on List
Scala REPL
Features of Scala REPL
- Apache Spark
Apache Spark Next-Generation Big Data Framework
History of Spark
Limitations of MapReduce in Hadoop
Introduction to Apache Spark
Components of Spark
Application of In-memory Processing
Hadoop Ecosystem vs Spark
Advantages of Spark
Spark Architecture
Spark Cluster in Real World
- Data warehouse in Hadoop
Hadoop and the Data Warehouse
Hadoop Differentiators
Data Warehouse Differentiators
When and Where to Use Which
- Augmenting Enterprise Data Warehouse
Introduction
RDBMS Strengths
RDBMS Weaknesses
Typical RDBMS Scenario
OLAP Database Limitations
Using Hadoop to Augment Existing Databases
Benefits of Hadoop
Hadoop Trade-offs
- Advance Programming in Hadoop
Advance Programming in Hadoop
- Writing MapReduce Program
A Sample MapReduce Program: Introduction
Map Reduce: List Processing
MapReduce Data Flow
The MapReduce Flow: Introduction
Basic MapReduce API Concepts
Putting Mapper & Reducer together in MapReduce
Our MapReduce Program: Word Count
Getting Data to the Mapper
Keys and Values are Objects
What is Writable Comparable
Writing MapReduce application in Java
The Driver
The Driver: Complete Code
The Driver: Import Statements
The Driver: Main Code
The Driver Class: Main Method
Sanity Checking the Job’s Invocation
Configuring the Job with Job Conf
Creating a New Job Conf Object
Naming the Job
Specifying Input and Output Directories
Specifying the Input Format
Determining Which Files to Read
Specifying Final Output with Output Format
Specify the Classes for Mapper and Reducer
Specify the Intermediate Data Types
Specify the Final Output Data Types
Running the Job
Reprise: Driver Code
The Mapper
The Mapper: Complete Code
The Mapper: import Statements
The Mapper: Main Code
The Map Method
The map Method: Processing the Line
Reprise: The Map Method
The Reducer
The Reducer: Complete Code
The Reducer: Import Statements
The Reducer: Main Code
The reduce Method
Processing the Values
Writing the Final Output
Reprise: The Reduce Method
Speeding up Hadoop development by using Eclipse
Integrated Development Environments
Using Eclipse
Writing a MapReduce program
- Introduction to Combiner
The Combiner
MapReduce Example: Word Count
Word Count with Combiner
Specifying a Combiner
Demonstration: Writing and Implementing a Combiner
- Sorting & Searching large data sets
Introduction
Sorting
Sorting as a Speed Test of Hadoop
Shuffle and Sort in MapReduce
Searching
- Performing a secondary sort
Secondary Sort: Motivation
Implementing the Secondary Sort
Secondary Sort: Example
- Indexing data and inverted Index
Indexing
Inverted Index Algorithm
Inverted Index: Data Flow
Aside: Word Count
- Term Frequency - Inverse Document Frequency (TF- IDF)
Term Frequency Inverse Document Frequency (TF-IDF)
TF-IDF: Motivation
TF-IDF: Data Mining Example
TF-IDF Formally Defined
Computing TF-IDF
- Calculating Word co- occurrences
Word Co-Occurrence: Motivation
Word Co-Occurrence: Algorithm
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FAQ's
Big Data is one of the accelerating and most promising fields that is being adopted by organizations of all kinds. Also, you will get huge salary packages.
- Software Developers
- Senior IT Professionals
- Mainframe professionals
- Data Engineers
- DBAs and DB professionals
- Testing professionals
- Software Architects
There are no such prerequisites for Big Data and Hadoop Course.
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We help in preparing a resume, assist in interview preparation, real-time projects to students, ensuring they can get placed in a leading MNC and corporate giant.
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- - Attend Mock-Up Interviews with Experts
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If yes, Register today and get impeccable Learning Solutions!
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Assignment
Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.
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WHAT OUR ALUMNI SAYS ABOUT US
Sachin Tyagi
Big Data Hadoop
Thanks for making this wonderful platform available. I would love to encourage more people to join Croma Learning Campus to fill the gap for their career needs. I took Big Data Hadoop Training from Croma and I must say that course content is just the great and well-structured as per the certificatio Read more...
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